7 papers
NeuroSeg Meets DINOv3: Transferring 2D Self-Supervised Visual Priors to 3D Neuron Segmentation via DINOv3 Initialization
Yik San Cheng, Runkai Zhao, Weidong Cai
2D visual foundation models, such as DINOv3, a self-supervised model trained on large-scale natural images, have demonstrated strong zero-shot generalization, capturing both rich g…
VirPro: Visual-referred Probabilistic Prompt Learning for Weakly-Supervised Monocular 3D Detection
Chupeng Liu, Jiyong Rao, Shangquan Sun +2
Monocular 3D object detection typically relies on pseudo-labeling techniques to reduce dependency on real-world annotations. Recent advances demonstrate that deterministic linguist…
BrainVista: Modeling Naturalistic Brain Dynamics as Multimodal Next-Token Prediction
Xuanhua Yin, Runkai Zhao, Lina Yao +1
Naturalistic fMRI characterizes the brain as a dynamic predictive engine driven by continuous sensory streams. However, modeling the causal forward evolution in realistic neural si…
Improving Multimodal Brain Encoding Model with Dynamic Subject-awareness Routing
Xuanhua Yin, Runkai Zhao, Weidong Cai
Naturalistic fMRI encoding must handle multimodal inputs, shifting fusion styles, and pronounced inter-subject variability. We introduce AFIRE (Agnostic Framework for Multimodal fM…
Unsupervised dMRI Artifact Detection via Angular Resolution Enhancement and Cycle Consistency Learning
Sheng Chen, Zihao Tang, Xinyi Wang +2
Diffusion magnetic resonance imaging (dMRI) is a crucial technique in neuroimaging studies, allowing for the non-invasive probing of the underlying structures of brain tissues. Cli…
Symmetry Awareness Encoded Deep Learning Framework for Brain Imaging Analysis
Yang Ma, Dongang Wang, Peilin Liu +4
The heterogeneity of neurological conditions, ranging from structural anomalies to functional impairments, presents a significant challenge in medical imaging analysis tasks. Moreo…
How Much Data are Enough? Investigating Dataset Requirements for Patch-Based Brain MRI Segmentation Tasks
Dongang Wang, Peilin Liu, Hengrui Wang +11
Training deep neural networks reliably requires access to large-scale datasets. However, obtaining such datasets can be challenging, especially in the context of neuroimaging analy…